The rest are still trying to figure out how to make it work.
Theres no questioning the promises ofmachine learningin nearly every sector.
But machine learning is not a magic wand.
Machine learning is about data
Machine learning models live on computing resources and data.
But data continues to remain a major hurdle in different stages of planning and adopting an AI strategy.
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Data problems crop up again when machine learning initiatives move from the research to the production phase.
Data quality remains the top barrier when it comes to using machine learning to extract valuable insights.
Eliminating silos should be a key priority in every machine learning initiative.
Lack of skill and difficulty in hiring was also a key barrier in adopting AI technologies.
And these shortcomings prevent them from making company-wide deployments of machine learning initiatives.
With the development of new machine learning and data science tools, the talent problem has become less intense.
Google, Microsoft, and Amazon have launched platforms that make it easier to develop machine learning models.
Another example isGoogles AutoML, which automates the tedious process of hyperparameter tuning.
Also, consider whether re-skilling is a possible course of action.
Only 38% of the Rackspace survey respondents relied on in-house talent to develop AI applications.
The rest were either fully outsourcing their AI projects or employing a combination of in-house and outsourced talent.
An example is C3.ai, an AI solutions provider that specializes in several industries.
C3.ai provides AI tools on top of existing cloud providers such as Amazon, Microsoft, and Google.
Strategy can sidestep the areas where AI and machine learning efforts may lose momentum or get lost in complexity.
Hands-on experts can also spare organizations from the messy work of cleanup and maintenance.
Such expertise, taken together, can make all the difference in finally achieving success.
Outsourcing AI talent must be done meticulously.
How do you evaluate your AI strategy?
Lack of commitment from executives was also among the top barriers.
AI often wanders around as a solution looking for a problem within organizations.
I believe this is one of the greatest impediments to its wide-scale adoption within organizations, DeVerter said.
Like any business ventureleadership needs to know how it will either help them save or make money.
Evaluating the outcome of AI initiatives is very difficult.
Understandably, this focus on quick profits is partly due to the high costs of AI initiatives.
According to the Rackspace survey, organizations spend a yearly average of $1.06 million on AI initiatives.
Decision-makers should have a clear picture of what their company will be embarking on.
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